An Integrated System to Select, Position, and Simulate Mobile Cranes for Complex Industrial Projects
Bibliographic record
Abstract
Determining feasible mobile crane configurations and positions on a complex industrial construction projects that are free of spatial conflicts and the subsequent scheduling of the lifts is important to the productivity and safety of a project. This paper focuses on the integration of a crane dimensional and capacity database with a project's lifted object information to select and position the cranes and then utilize expert knowledge to simulate the heavy lift plan. The system uses dimensional and coordinate data instead of CAD software for computation of crane position areas amongst known obstructions and boundary limits. The crane position area is specific to a crane model and identifies where the center of rotation of the crane can be placed without having any part of the crane body contact the known obstructions and boundary limits. This analysis is performed for multiple cranes and lifted object scenarios to develop a list of possible options. Schedule date constraints and logic are used to simulate and optimize the schedule and crane selection. This provides the practitioner with an effective planning tool to select, position and schedule cranes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".